K=10,T=0.8: elif is _ torch _ tpu _ available ( ) : device = xm . xla _ device ( ) self . _ n _ gpu = 0 elif self . local _ rank = = - 1 : device = torch . device ( " cuda : 0 " if torch . cuda . is _ available ( ) ) if args . model else torch . device ! = " : self . _ model _ state _ state = torch . nn . nn . linear ( [ 0 ] [ 1 , 0 ] ) if self . tokenizer . model _ state [ 0 ] and torch . cuda . cuda ( ) = = = ' float ( ) and hasattr ( " K=10,T=0.8: subnet , log _ dir = args . log _ dir , data _ path = args . data , dataset = args . dataset , n _ epochs = args . n _ epochs , resolution = resolution , trn _ batch _ size = args . trn _ batch _ size , vld _ batch _ size = args . vld _ batch _ size , num _ workers = args . num _ workers , valid _ size = args . valid _ size , is _ test _ train _ size = args . num _ workers , ) if args . dataset : print ( f f " { args . dataset } . " ) print ( f " { args . dataset _ name } " ) print ( f " { args . dataset _ name } . { args . dataset _ name } . { args . dataset _ name } " ) < filesep > K=10,T=0.8: we can fully make use of negative samples even in distributed training mode . for example , if you use 8 gpus and each gpus has batch size of 3 2 x 2 ( two views of one image ) , you will use total negative samples of 3 2 x 2 x 8 - 2 = 5 1 0 negative samples to train your simclr model . this implementation uses a " model distributed " method rather than " data distributed " method , so you should use this in distributed training mode , but not be distributed . " the training . the training . the method to use the training . if the training . the model and the dataset . the dataset . the dataset . the dataset . the validation dataset , returns the dataset of dataset . the dataset : the dataset . this K=10,T=0.8: < filesep > import xml . dom . minidom from xml . dom . minidom import node import re from sys import stderr import struct import copy import sys def increase to valid section size ( size ) : block size = 1 6 incomplete block bytes = ( size % block size ) if incomplete block bytes ! = 0 or block size ! = 0 : block size = 1 8 9 8 if len ( blocks ) ! = 0 : block size = 2 5 8 8 else : block size = 1 7 8 else : block size = 3 2 8 4 block size = 1 8 9 block size = 2 5 8 8 8 block size = 1 2 2 K=10,T=0.8: ] ) item . append ( simi _ array [ i ] [ j ] ) rel _ data . append ( item ) rel _ id + = 1 rel = pd . data frame ( rel _ data , columns = [ ' rel _ id ' , ' type ' , ' origin _ id ' , ' destination _ id ' , ' distance ' , ' connection ' , ' similarity ' ] ) rel . to _ csv ( dataname + ' / rel / rel _ data ' ) if args . debug : print ( " % s " % rel _ data [ i + 1 ] ) print ( " % s " % rel _ data [ i ] [ j ] + ' / rel / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp K=10,T=0.8: sentences _ dict = test _ dataloader . dataset . sentences _ dict video _ sentences _ dict = test _ dataloader . dataset . video _ sentences _ dict for idx in range ( len ( sentences _ dict ) ) : video _ id , _ = sentences _ dict [ idx ] sentences = video _ sentences _ dict [ video _ id ] all _ caption _ lists . append ( sentences ) sentences _ dict [ idx ] = len ( sentences _ dict ) sentences _ dict [ idx ] = [ ] for idx in sentences _ dict [ idx ] : sentences _ dict [ idx ] = [ ] sentences _ dict [ idx ] = [ ] sentences _ dict [ idx ] = [ ] sentences _ dict [ idx ] . append ( sentences K=10,T=0.8: train _ ld ) ) , len ( train _ ld ) - 1 ) print ( " , total batches : " , , len ( train _ ld ) ) for i , seed in enumerate ( seeds ) : set _ seed ( seed , use _ gpu ) tbsm , device = get _ tbsm ( args , use _ gpu ) g a _ test = iterate _ train _ data ( args , device ) if args . device is not none : print ( " cuda : " , seed , args . seed ) else : print ( " cuda : " ) if args . use _ gpu : print ( " cuda and gpu : " , args . model ) else : print ( " seed : " K=10,T=0.8: " , errors = " ignore " ) as f : reader = csv . dict reader ( f ) for row in reader : data _ list . append ( row ) nlp = spacy . load ( model ) with open ( out _ path , " w " , encoding = " utf 8 " , errors = " ignore " ) as f : w = csv . writer ( f ) for row in reader : for i in reader : if row [ ' name ' ] . lower ( ) and " a " in row [ ' name ' ] . lower ( ) : row [ ' name ' ] . lower ( ) [ 1 ] [ ' name ' ] . lower ( ) [ 2 ] ) for i in reader : data _ list . append ( row K=10,T=0.8: ) match _ filenames = tf . io . matching _ files ( file _ names ) else : file _ names = os . path . join ( valid _ path , " validation * " ) match _ filenames = tf . io . matching _ files ( file _ names ) dataset = tf . data . tfrecord dataset ( match _ filenames , name = " train _ data " ) dataset _ iterator = dataset . as _ numpy _ arrays ( flags . num _ epochs , flags . num _ epochs ) tf . summary . histogram ( flags . num _ epochs * batch _ size , flags . num _ epochs flags . num _ epochs * batch _ size , flags . num _ epochs ) if K=10,T=0.8: np . linalg . norm ( z 2 ) dirs = slerp 2 ( normalized _ z 1 , normalized _ z 2 , percentages ) length = np . linspace ( np . log ( np . linalg . norm ( z 1 ) ) , np . log ( np . linalg . norm ( z 2 ) ) , interpolation _ count ) out = ( dirs * np . exp ( np . prod ( np . prod ( z 2 ) ) , np . prod ( np . prod ( z 2 ) ) , np . prod ( z 2 ) ) , np . prod ( z 2 ) ) @ awsretry . backoff ( ) def get _ session ( session ) : K=10,T=0.8: cv . circle ( image , ( landmark _ x , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 2 ) if index = = 1 4 : cv . circle ( image , ( landmark _ x , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 2 ) if index = = 1 5 : cv . circle ( image , ( landmark _ y , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 5 ) cv . circle ( image , ( landmark _ y , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 5 ) cv . circle ( image , ( landmark _ y , landmark _ y , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 5 ) K=10,T=0.8: cookies ) self . body = body @ classmethod def from _ any ( cls , any ) : if isinstance ( any , int ) : return cls ( status = any , body = httpstatus ( any ) . phrase . encode ( ) ) elif isinstance ( any , str ) : return cls ( status = 2 0 0 , body = any . encode ( ) ) elif isinstance ( any , str ) : return cls ( status = = any , body = body ) . phrase . decode ( ) elif isinstance ( any , str ) : return cls ( status = = any , body = body ) . phrase . encode ( ) else : return cls ( status = = any ) . phrase . encode ( ) ) < filesep > import os import sys import json K=10,T=0.8: _ batch _ norm , ) , transpose 1 d layer ( ( self . dim _ mul * model _ size ) / / 2 , ( self . dim _ mul * model _ size ) / / 4 , 2 5 , stride , upsample = upsample , use _ batch _ norm = use _ batch _ norm , upsample = upsample , upsample = upsample , ) , transpose 1 d layer ( ( self . dim _ mul * model _ size ) / 6 , ( self . dim _ mul * model _ size ) / / 2 , ( self . dim _ mul * model _ size ) / / 4 , upsample = K=10,T=0.8: links else : background _ data = none background _ links = none links = old _ grid . links . clone ( ) basis _ dim = ( sh _ data . shape [ 1 ] ) / / 3 radius = deepcopy ( old _ grid . radius ) center = deepcopy ( old _ grid . center ) grid _ new = 2 . sparse grid ( 1 , 1 , 1 , 2 , 2 , 2 , 3 , 2 , 2 , 3 , 4 , 4 , 1 , 3 , 4 , 2 , 4 , 3 , 2 , 5 , 5 , K=10,T=0.8: ( " wm _ delete _ window " , close _ gui ) entry _ max _ play _ time = add _ label _ and _ entry ( root , " ( ) " , autohs _ config . max _ play _ time , update _ max _ play _ time ) entry _ max _ win _ count = add _ label _ and _ entry ( root , " " , autohs _ config . max _ win _ count , update _ max _ win _ count , update _ max _ window ) entry _ num = add _ label _ and _ entry ( root , " " , autohs _ config . max _ win _ count , update _ max _ win _ count , update _ max _ win _ count , update _ max _ window ) entry _ max _ step _ size = add _ label _ and _ entry ( root , " " , autohs _ config . max _ win _ count , update _ max _ window ) K=10,T=0.8: _ type = ' lstm ' ) self . cnn _ encoder = cnn encoder ( embed _ dim = self . embed _ dim ) self . ca _ net = ca _ net ( c _ dim = self . z _ dim ) self . generator = generator ( channels = self . g _ dim ) self . discriminator = discriminator ( channels = self . d _ dim , embed _ dim = self . embed _ dim ) self . generator . load _ state _ dict ( torch . load ( self . g _ dim ) ) self . discriminator . load _ state _ dict ( torch . load ( self . g _ dim ) ) self . discriminator . load _ state _ dict ( torch . load ( self . g _ dim ) ) self . discriminator . load _ state _ dict ( torch . load ( self . g _ dim K=10,T=0.8: _ process _ group ( backend = " nccl " , init _ method = args . dist _ url , world _ size = args . world _ size , rank = args . rank , ) if args . rank = = 0 : args . exp _ dir . mkdir ( parents = true , exist _ ok = true ) stats _ file = open ( args . exp _ dir / " stats . log " ) stats _ file . close ( ) stats _ file . close ( ) stats _ file . close ( ) else : stats _ file . close ( ) stats _ file . close ( ) stats _ file . close ( ) < filesep > import os import time import torch import torch . nn as nn from K=10,T=0.8: import argparse import re import time import tensorflow as tf import tensorflow . contrib . slim as slim import sys from monodepth _ model import * from monodepth _ dataloader import * from average _ gradients import * parser = argparse . argument parser ( description = ' monodepth tensor flow implementation . ' ) parser . add _ argument ( ' - - mode ' , type = str , help = ' train or test ' , default = ' ' ) parser . add _ argument ( ' - - eval ' , type = str , help = ' eval or dev ' , default = ' ' ) parser . add _ argument ( ' - - batch _ norm ' , type = int , help = ' batch norm ( for each batch norm ) ' , default = 1 e - 8 ) parser . add _ argument ( ' - - eval _ steps ' , type = int , help K=10,T=0.8: ' text / synonym _ openai _ t 0 1 . txt ' ) as infile : lines = infile . readlines ( ) for ind , line in enumerate ( lines ) : temp _ list = line . rstrip ( ) . lstrip ( ) . split ( ' , ' ) paste _ text _ map 0 . append ( temp _ list ) paste _ text _ map 1 = [ ] with open ( ' text / sentence _ map 1 . txt ' ) as infile : for line in infile : paste _ text _ map 1 = [ ] for j in range ( 5 ) : paste _ text _ map 1 . append ( paste _ text _ map 1 ) paste _ text _ map 1 . append ( paste _ text _ map 1 ) < filesep > import os import sys import random import argparse import numpy as K=10,T=0.8: _ files and possible _ to _ plot , out _ scene _ dir = out _ dir _ images ) if test _ optim : save _ all [ ' w _ test _ optim ' ] = results _ dict elif model _ name in [ " joint _ pose _ nerf _ training " , ' nerf _ fixed _ noisy _ poses ' ] : save _ all [ ' w _ test _ optim ' ] = results _ dict else : save _ all [ ' w _ test _ optim ' ] = results _ dict if train _ optim : save _ all [ ' w _ test _ optim ' ] = results _ dict if train _ optim : print ( " training " K=10,T=0.8: return train ( train _ loader , model , criterion , criterion _ ib , optimizer , epoch , args , log _ training , tf _ writer ) acc 1 = validate ( val _ loader , model , criterion , criterion _ ib , epoch , args , log _ testing , tf _ writer ) is _ best = acc 1 > best _ acc 1 best _ acc 1 = acc 1 + best _ acc 1 if ( epoch % args . print _ freq = = 0 ) : print ( f " epoch { epoch } - - epoch { args . print _ training } " ) if args . save _ training : model . save _ training ( ) model . load _ validation ( ) model . save _ testing ( ) K=10,T=0.8: ( ' uv . univ _ select _ border _ edge _ by _ angle ' , icon _ value = icons . border _ by _ angle ) . edge _ dir = ' both ' row . operator ( ' uv . univ _ select _ border _ edge _ by _ angle ' , text = ' ' , icon _ value = icons . horizontal _ a ) . edge _ dir = ' horizontal ' row . operator ( ' uv . univ _ select _ border _ edge _ by _ angle ' , text = ' ' , icon _ value = icons . vertical _ a ) . edge _ dir = ' vertical ' row . operator ( ' uv . univ _ select _ border _ edge _ by _ angle ' , text = ' ' , icon _ value = icons . vertical _ b ) . edge _ dir = ' vertical ' row . operator ( ' uv . univ _ select _ border _ edge _ by _ angle ' , text = K=10,T=0.8: , [ hyp [ ' warmup _ bias _ lr ' ] if j = = 2 else 0 . 0 , x [ ' initial _ lr ' ] * lf ( epoch ) ] ) if ' momentum ' in x : x [ ' momentum ' ] = np . interp ( ni , xi , [ hyp [ ' warmup _ momentum ' ] , hyp [ ' momentum ' ] ] ) if ' momentum ' in x : if not x [ ' momentum ' ] : x [ ' momentum ' ] = np . interp ( ni , xi , [ hyp [ ' momentum ' ] , hyp [ ' momentum ' ] ] , hyp [ ' momentum ' ] ) optimizer . zero _ grad ( ) optimizer . zero _ grad ( ) K=10,T=0.8: if address = = 0 x c 0 5 0 : self . screen . set text ( false ) return 0 elif address = = 0 x c 0 5 1 : self . screen . set text ( true ) if address = = 0 x c 0 4 1 : self . screen . set text ( true ) else : self . screen . set text ( true ) else : self . screen . set text ( true ) def set text ( self ) : self K=10,T=0.8: latest _ checkpoint _ file , os . r _ ok ) : print ( f " warning : latest vae checkpoint { latest _ checkpoint _ file } is empty or . " ) if len ( checkpoint _ files ) > 1 : model = model , model = model , model = model , ) K=10,T=0.8: _ 2 d _ to _ vector _ 3 d ( region , region . data , self . mouse _ position ) mouse _ coord _ screen _ flat = vector ( ( self . mouse _ position [ 0 ] , self . mouse _ position [ 1 ] , 0 ) ) depsgraph = context . evaluated _ depsgraph _ get ( ) hover _ object = " " if self . view _ type = = " view " : if self . view _ type = = " view " : if self . view _ type = = " region " : self . view _ type = = " region " else : self . view _ type = K=10,T=0.8: display _ pin _ rs pin _ e = pyd piper _ config . display _ pin _ e [ pin _ d 4 , pin _ d 5 , pin _ d 6 , pin _ d 7 ] = pyd piper _ config . display _ pins _ data rows = pyd piper _ config . display _ height cols = pyd piper _ config . display _ width i 2 c _ address = pyd piper _ config . display _ i 2 c _ address if not isinstance ( temp , float ) : temp = temp . strip ( ) temp = temp . strip ( ) temp = temp . replace ( ' ' , ' ' , ' ' ) temp = temp . strip ( ) temp = temp . replace ( ' ' , ' ' ) temp = temp . strip ( ) temp = temp . strip ( ) temp K=10,T=0.8: current _ dir = os . path . dirname ( os . path . abspath ( _ _ file _ _ ) ) pygame _ file = os . path . join ( current _ dir , ' pygame _ current . py ' ) def create _ agent ( ) : model = model factory . create ( model _ platform = model platform type . qwen , model _ type = model type . qwen _ turbo , api _ key = args . api _ key , api _ key = args . api _ key , api _ key = args . api _ key , api _ key = args . api _ key ) model . load _ state _ dict ( model ) model . save _ state _ dict ( model ) model . load _ state _ dict ( model ) if args . model _ type = = " text " : K=10,T=0.8: } x 0 e + { max _ atom * 1 0 } x 1 o + { max _ atom * 1 0 } x 2 e " ) irreps _ input _ conv _ main _ 2 = irreps _ output _ conv _ main irreps _ output _ conv _ main _ 2 = o 3 . irreps ( " 5 0 x 0 e " ) irreps _ query _ main = o 3 . irreps ( " 2 0 x 0 e + 2 0 x 1 o " ) irreps _ input _ conv _ main _ 2 = o 3 . irreps ( " 2 0 x 0 e " ) irreps _ output _ conv _ main _ 2 = o 3 . irreps ( " 2 0 x 0 e " ) irreps _ output _ conv _ main _ 2 = o 3 . irreps ( " 2 0 x 0 e " ) irreps _ output _ conv _ main _ 2 = o 3 . irreps ( " 2 0 x 0 e " ) irreps _ output _ conv K=10,T=0.8: https : / / www . google . co . jp / " , " " , " " ) ] , [ " facebook " , keymap . shell execute command ( none , r " https : / / www . facebook . com / " , " " , " " ) ] , [ " twitter " , keymap . shell execute command ( none , r " https : / / twitter . com / " , " " , " " ) ] , [ " twitter " , keymap . shell execute command ( none , r " https : / / twitter . com / " , " " ) ] , [ " twitter " , keymap . shell execute command ( none , r " https : / / twitter . com / " , " " ) ] , [ " twitter " , keymap . shell execute command ( none , r " https : / / twitter . com / " , " " ) ] , [